Performance analysis of signal processing algorithms for wall clutter mitigation and contrast target detection
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Abstract The presence of clutter in through‐the‐wall images significantly degrades image quality and hampers the performance of algorithms used for data processing in tasks such as target detection, identification, or reconstruction. Real building walls exhibit inherent inhomogeneities, featuring varying frequency and spatial properties that defy the assumption of a smooth surface. Consequently, they produce nonuniform wall surface reflections at each scanning position. Furthermore, these walls often incorporate supply pipes that introduce substantial clutter, which, although stronger than the target responses, remains weaker than the wall surface clutter. Additionally, this clutter often exhibits signatures similar to those of the target. In real‐world scenarios, target reflections can manifest as wide, flat hyperbolas or nearly straight lines, and they may be positioned near the wall. Consequently, distinguishing targets from clutter becomes a complex challenge. Various clutter reduction methods have been proposed in recent years, showing varying degrees of success. However, the effectiveness of these methods in the literature is inconsistent and sometimes contradictory. To address this issue, a comprehensive investigation of well‐established clutter reduction methods was conducted to evaluate their performance under identical conditions. These methods were rigorously assessed using practical radar‐measured data acquired in the presence of actual building wall materials and contrasting targets. Evaluation criteria include the target‐to‐clutter ratio and peak signal‐to‐noise ratio. The results of the experiment revealed that independent component analysis outperformed other clutter reduction methods, demonstrating superior performance in mitigating clutter and enhancing target detection.Keywords:
Stationary target indication
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Based on the correlation of sea clutter of adjacent range cells,the target fuzzy distance caused by different coherent accumulation time is analyzed and the sea clutter of adjacent range cell cancellation algorithm is improved.This method has overcome the problem of target cancellation caused by the neighboring cell sea clutter cancellation.By using the results from the clutter suppression processing proposed in the paper to the CFAR ship target detection algorithm,the false alarm rate of the target detection could be obviously reduced.
Stationary target indication
Radar horizon
Moving target indication
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In this paper we propose a new differential clutter map processing to reduce the false alarms from stationary ground clutter, sea clutter and improve the detection probability of the slow moving target with low Doppler frequency shift. The proposed clutter map is a scan to scan processing which receives the echo signal from Doppler filter bank tuned at lowest Doppler frequency, processes it and delivers the processed signal to the CFAR processing. The simulation results show that the false alarm rate is reduced by more than -30 db. The Clutter map also increased the probability of detection of low speed target, slower than 100 Km/hr.
Moving target indication
Stationary target indication
Doppler frequency
False alarm
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Simple empirical models are described which enable the radar designer to determine single-scan detection probabilities from the signal-to-clutter ratio and false alarm probability, for the compound K-distribution clutter model. The method to apply these results is discussed in detail, and the variability of the clutter rejection coefficient is briefly reviewed. Results covering the following are presented: the average clutter reflectivity, the shape parameter for the K-distributed clutter amplitude distribution, detection performance for targets in clutter, and evaluation of performance in clutter and noise.< >
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Stationary target indication
Radar detection
False alarm
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Mitigation of clutter due to sidelobe and mainlobe of antenna in airborne radars is of paramount importance for detection of moving targets. In this paper, an algorithm based on mathematical morphology is proposed to remove clutter from range - Doppler image. Clutter spread in range as well as Doppler due to motion of platform and clutter characteristics are dependent on range. The proposed algorithm extracts the clutter region without training or prior knowledge of the clutter spectrum. Morphological processing is applied on range-Doppler image to find the connected components of detections from constant false alarm rate (CFAR) processing. The identified connected components form the clutter image which is then used to remove false detections due to clutter. The proposed algorithm is compared with conventional sidelobe blanking algorithm and has shown better performance.
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System requirements for harbor surveillance radars are reviewed. Experimental sea clutter data are presented and shown to be closely matched by a log-normal clutter model. Characteristics and parameters of the log-normal clutter model are described. Detection performance against a steady target in a log-normal clutter background, using logarithmic receivers, is provided. A log-normal target model is described. Detection curves for log-normal fluctuating targets in log-normal clutter are developed. A Constant False Alarm Rate (CFAR) processor that adaptively functions in log-normal clutter is described. CFAR detection performance is derived and presented in the form of CFAR loss curves.
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Radar detection
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The paper carries on the brief sea-clutter frequency spectrum analysis to sea-clutter data collected by a certain shipborne search radar,and makes a comparison between the frequency spectrum of echo signals in the sea area with ships and the sea area without ships,adopts moving target detection(MTD) and constant false alarm processing by frequency domain clutter chart to confront with the sea clutter,and puts forward the improved scheme combined the constant false alarm processing by frequency domain clutter chart with unit average constant false alarm according to the problem that the false alarm rate increases in the constant false alarm processing by frequency domain clutter chart.
Stationary target indication
Moving target indication
False alarm
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Clutter is defined as any unwanted radar return. The presence of clutter in a range/Doppler cell complicates the detection of a target return signal in that cell. In order to quantify the effect of clutter on the probability of detection, we must first specify sets of models suitable for representing the clutter and target. The simplest and most common model for clutter is based on the gamma density. We include two additional models, the NCG and NCGG clutter models for low grazing angles. They are motivated by physical arguments, the latter of which can accommodate the well-known phenomenon of speckle. Using one of these models for clutter together with one of several models for targets, we determine, in a range/Doppler cell, expressions for probabilities of detection of a target in the presence of clutter. It is important to control the probability of false alarms. The presence of clutter in a cell necessitates an increase in the detection threshold setting in order to control false alarms, thus lowering the probability of detection. If the clutter level is unknown, then we need to take measurements of the clutter and use it to adjust the threshold. The more clutter samples we take, the better the estimate of the clutter level and the less is the resulting detection loss. Using the expressions for the probability of detection in clutter, we can quantify the detection loss for a pair of commonly used constant false-alarm rate (CFAR) techniques and investigate how the loss varies with different parameter values, especially with regard to the number of clutter samples taken to estimate the clutter level.
Stationary target indication
Moving target indication
Radar horizon
False alarm
Statistical power
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A statistical model for the complex data of high range resolution surface radars is proposed that incorporates range coherence in both the clutter and target. The clutter is modeled using two distributions: a background clutter distribution and wave clutter distribution. The model is then used to develop a multi-hypothesis detector that discriminates between targets, wave clutter, and background clutter. Performance on real surface radar data shows that this technique reduces false alarms due to wave clutter spikes more effectively than the cell-averaging constant false alarm rate (CA-CFAR) detector that operates on the amplitude data using a composite clutter model.
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Simple empirical models are described which enable the radar designer to determine single-scan detection probabilities from the signal-to-clutter ratio and false alarm probability, for the compound K-distribution clutter model. The method to apply these results is discussed in detail, and the variability of the clutter rejection coefficient is briefly reviewed. Results covering the following are presented: the average clutter reflectivity, the shape parameter for the K-distributed clutter amplitude distribution, detection performance for targets in clutter, and evaluation of performance in clutter and noise. >
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K-distribution
False alarm
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AbstrcatA novel biparametric OS-CFAR(BOS-CFAR) detection method for AEW radar in the sea clutter is proposed.The performance is discussed and analyzed for multiple-pulse noncoherent integration when this method operates in the multiple-target environment in the non-Gaussian sea clutter background. Theoretical analysis and simulation result show that the proposed method can enhance the CFAR detection performance for AEW radar in the sea clutter background effectively.
Radar detection
Radar horizon
Stationary target indication
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